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Abstract

A web based Spatial Decision Support System (web SDSS) has been implemented in Thessaly, the most significant arable cropping region in Greece, to evaluate energy crop supply. The web SDSS uses an optimization module to support the decision process launching mathematical programming (MP) profit maximizing farm models. Energy to biomass raw material cost is provided in supply curve form incorporating physical land suitability for crops, farm structure, and Common Agricultural Policy (CAP) scenarios. To generate biomass supply curves, the optimization problem is parametrically solved for a number of steps within a price range determined by the user. The more advanced technique used to solve the MP model, the higher the delay of response to the user. In this paper, the authors examine how effectively the web SDSS response time can be reduced to the user requests using parallel solving of the corresponding optimization problem. The results are encouraging, since the total solution time drops significantly as the problem’s size increases, improving the users’ experience even when the underlying optimization models use advanced, time demanding modeling techniques. These statements are illustrated by comparing linear and non-linear agricultural sector models.

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Introduction

The progress in Web-based decision support technologies has been recently described by Bhargava et al. (2007) who distinguish between model-driven and data-driven decision support system (DSS) to provide an impressive list of systems for decision support using the web as a medium (stand-alone commercial applications) or as a computer (web-DSS). Most applications concern business decision support, whereas some deal with environmental issues involving also multi-criteria models often attempting to enhance public participation in local environmental decision making (Kingston et al., 2000). One of the most interesting classes of web-based decision support tools are the so-called Spatial DSS (SDSS). SDSS, as defined by Sugumaran and Sugumaran (2007), are “flexibly integrated systems built on a GIS platform to deal with spatial data and manipulations, along with an analysis module ... they support ‘what if’ analysis ... and help the user in understanding the results” (p. 850). With the development of the internet, Web-based SDSS have been developed, adding Internet interface programs to the computational models and geographic databases of the SDSS, in order to provide decision support through the Web based on relevant information.

Bio-energy issues constitute by excellence spatially dependent problems requiring both detailed spatial information but also extensive model building. Unlike conventional energy carriers that have hierarchical structure, biomass-to-energy production involves hundreds to often thousands of decentralised decision makers. This is considered one of the “grand challenges” for bio-energy assessment (McKone et al., 2011). As a matter of fact, bio-energy profitability is linked to the structure and perspectives of the arable cropping systems to supply considerable quantities of a bulky raw material to transformation plants also taking into account demand location and volume. Recent analyses of economic biomass potential are reported in regional (Hilst et al., 2010) or country level (Simon et al., 2010). Therefore, appropriate tools are necessary to enable comprehensive analysis and support decisions of policy makers, industry, researchers and farmers. For this purpose, a state-of-the-art modular SDSS that contains optimization models embedded in a GIS environment fed by technical, economic, and cartographic databases has been built to provide stakeholders with region specific biomass-to-energy supply information in Central Greece (Rozakis, 2010). A web-based interface built in open source software makes the SDSS tool available for collaborative decision-making allowing for an interactive process in real time. The tool operates on the Internet, where the user can have access to the dataset, enter selected parameters into the model, and enable spatial visualization and exploration of the results, injecting interactivity in the decision process.